Methods

compile

optimizer: String (name of optimizer) or optimizer instance.
See optimizers.

loss: String (name of objective function) or objective function.
See losses.
If the model has multiple outputs, you can use a different loss
on each output by passing a dictionary or a list of losses.
The loss value that will be minimized by the model
will then be the sum of all individual losses.

metrics: List of metrics to be evaluated by the model
during training and testing.
Typically you will use metrics=['accuracy'].
To specify different metrics for different outputs of a
multi-output model, you could also pass a dictionary,
such as metrics={'output_a': 'accuracy'}.

loss_weights: Optional list or dictionary specifying scalar
coefficients (Python floats) to weight the loss contributions
of different model outputs.
The loss value that will be minimized by the model
will then be the weighted sum of all individual losses,
weighted by the loss_weights coefficients.
If a list, it is expected to have a 1:1 mapping
to the model's outputs. If a tensor, it is expected to map
output names (strings) to scalar coefficients.

sample_weight_mode: If you need to do timestep-wise
sample weighting (2D weights), set this to "temporal".
None defaults to sample-wise weights (1D).
If the model has multiple outputs, you can use a different
sample_weight_mode on each output by passing a
dictionary or a list of modes.

weighted_metrics: List of metrics to be evaluated and weighted
by sample_weight or class_weight during training and testing.

target_tensors: By default, Keras will create placeholders for the
model's target, which will be fed with the target data during
training. If instead you would like to use your own
target tensors (in turn, Keras will not expect external
Numpy data for these targets at training time), you
can specify them via the target_tensors argument. It can be
a single tensor (for a single-output model), a list of tensors,
or a dict mapping output names to target tensors.

**kwargs: When using the Theano/CNTK backends, these arguments
are passed into K.function.
When using the TensorFlow backend,
these arguments are passed into tf.Session.run.

Raises

ValueError: In case of invalid arguments for
optimizer, loss, metrics or sample_weight_mode.

Trains the model for a given number of epochs (iterations on a dataset).

Arguments

x: Numpy array of training data (if the model has a single input),
or list of Numpy arrays (if the model has multiple inputs).
If input layers in the model are named, you can also pass a
dictionary mapping input names to Numpy arrays.
x can be None (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).

y: Numpy array of target (label) data
(if the model has a single output),
or list of Numpy arrays (if the model has multiple outputs).
If output layers in the model are named, you can also pass a
dictionary mapping output names to Numpy arrays.
y can be None (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).

batch_size: Integer or None.
Number of samples per gradient update.
If unspecified, batch_size will default to 32.

epochs: Integer. Number of epochs to train the model.
An epoch is an iteration over the entire x and y
data provided.
Note that in conjunction with initial_epoch,
epochs is to be understood as "final epoch".
The model is not trained for a number of iterations
given by epochs, but merely until the epoch
of index epochs is reached.

callbacks: List of keras.callbacks.Callback instances.
List of callbacks to apply during training.
See callbacks.

validation_split: Float between 0 and 1.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the x and y data provided, before shuffling.

validation_data: tuple (x_val, y_val) or tuple
(x_val, y_val, val_sample_weights) on which to evaluate
the loss and any model metrics at the end of each epoch.
The model will not be trained on this data.
validation_data will override validation_split.

shuffle: Boolean (whether to shuffle the training data
before each epoch) or str (for 'batch').
'batch' is a special option for dealing with the
limitations of HDF5 data; it shuffles in batch-sized chunks.
Has no effect when steps_per_epoch is not None.

class_weight: Optional dictionary mapping class indices (integers)
to a weight (float) value, used for weighting the loss function
(during training only).
This can be useful to tell the model to
"pay more attention" to samples from
an under-represented class.

sample_weight: Optional Numpy array of weights for
the training samples, used for weighting the loss function
(during training only). You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
(samples, sequence_length),
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile().

initial_epoch: Integer.
Epoch at which to start training
(useful for resuming a previous training run).

steps_per_epoch: Integer or None.
Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. When training with input tensors such as
TensorFlow data tensors, the default None is equal to
the number of samples in your dataset divided by
the batch size, or 1 if that cannot be determined.

validation_steps: Only relevant if steps_per_epoch
is specified. Total number of steps (batches of samples)
to validate before stopping.

Returns

A History object. Its History.history attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).

Raises

RuntimeError: If the model was never compiled.
ValueError: In case of mismatch between the provided input data
and what the model expects.

evaluate

x: Numpy array of test data (if the model has a single input),
or list of Numpy arrays (if the model has multiple inputs).
If input layers in the model are named, you can also pass a
dictionary mapping input names to Numpy arrays.
x can be None (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).

y: Numpy array of target (label) data
(if the model has a single output),
or list of Numpy arrays (if the model has multiple outputs).
If output layers in the model are named, you can also pass a
dictionary mapping output names to Numpy arrays.
y can be None (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).

batch_size: Integer or None.
Number of samples per evaluation step.
If unspecified, batch_size will default to 32.

verbose: 0 or 1. Verbosity mode.
0 = silent, 1 = progress bar.

sample_weight: Optional Numpy array of weights for
the test samples, used for weighting the loss function.
You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
(samples, sequence_length),
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile().

steps: Integer or None.
Total number of steps (batches of samples)
before declaring the evaluation round finished.
Ignored with the default value of None.

Returns

Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.

predict

predict(x, batch_size=None, verbose=0, steps=None)

Generates output predictions for the input samples.

Computation is done in batches.

Arguments

x: The input data, as a Numpy array
(or list of Numpy arrays if the model has multiple inputs).

batch_size: Integer. If unspecified, it will default to 32.

verbose: Verbosity mode, 0 or 1.

steps: Total number of steps (batches of samples)
before declaring the prediction round finished.
Ignored with the default value of None.

Returns

Numpy array(s) of predictions.

Raises

ValueError: In case of mismatch between the provided
input data and the model's expectations,
or in case a stateful model receives a number of samples
that is not a multiple of the batch size.

train_on_batch

train_on_batch(x, y, sample_weight=None, class_weight=None)

Runs a single gradient update on a single batch of data.

Arguments

x: Numpy array of training data,
or list of Numpy arrays if the model has multiple inputs.
If all inputs in the model are named,
you can also pass a dictionary
mapping input names to Numpy arrays.

y: Numpy array of target data,
or list of Numpy arrays if the model has multiple outputs.
If all outputs in the model are named,
you can also pass a dictionary
mapping output names to Numpy arrays.

sample_weight: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample.
In the case of temporal data, you can pass a 2D array
with shape (samples, sequence_length),
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile().

class_weight: Optional dictionary mapping
class indices (integers) to
a weight (float) to apply to the model's loss for the samples
from this class during training.
This can be useful to tell the model to "pay more attention" to
samples from an under-represented class.

Returns

Scalar training loss
(if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.

test_on_batch

test_on_batch(x, y, sample_weight=None)

Test the model on a single batch of samples.

Arguments

x: Numpy array of test data,
or list of Numpy arrays if the model has multiple inputs.
If all inputs in the model are named,
you can also pass a dictionary
mapping input names to Numpy arrays.

y: Numpy array of target data,
or list of Numpy arrays if the model has multiple outputs.
If all outputs in the model are named,
you can also pass a dictionary
mapping output names to Numpy arrays.

sample_weight: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample.
In the case of temporal data, you can pass a 2D array
with shape (samples, sequence_length),
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile().

Returns

Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.

Trains the model on data generated batch-by-batch by a Python generator
(or an instance of Sequence).

The generator is run in parallel to the model, for efficiency.
For instance, this allows you to do real-time data augmentation
on images on CPU in parallel to training your model on GPU.

The use of keras.utils.Sequence guarantees the ordering
and guarantees the single use of every input per epoch when
using use_multiprocessing=True.

Arguments

generator: A generator or an instance of Sequence
(keras.utils.Sequence) object in order to avoid
duplicate data when using multiprocessing.
The output of the generator must be either

a tuple (inputs, targets)

a tuple (inputs, targets, sample_weights).

This tuple (a single output of the generator) makes a single
batch. Therefore, all arrays in this tuple must have the same
length (equal to the size of this batch). Different batches may
have different sizes. For example, the last batch of the epoch
is commonly smaller than the others, if the size of the dataset
is not divisible by the batch size.
The generator is expected to loop over its data
indefinitely. An epoch finishes when steps_per_epoch
batches have been seen by the model.

steps_per_epoch: Integer.
Total number of steps (batches of samples)
to yield from generator before declaring one epoch
finished and starting the next epoch. It should typically
be equal to the number of samples of your dataset
divided by the batch size.
Optional for Sequence: if unspecified, will use
the len(generator) as a number of steps.

epochs: Integer. Number of epochs to train the model.
An epoch is an iteration over the entire data provided,
as defined by steps_per_epoch.
Note that in conjunction with initial_epoch,
epochs is to be understood as "final epoch".
The model is not trained for a number of iterations
given by epochs, but merely until the epoch
of index epochs is reached.

callbacks: List of keras.callbacks.Callback instances.
List of callbacks to apply during training.
See callbacks.

validation_data: This can be either

a generator or a Sequence object for the validation data

tuple (x_val, y_val)

tuple (x_val, y_val, val_sample_weights)

on which to evaluate
the loss and any model metrics at the end of each epoch.
The model will not be trained on this data.

validation_steps: Only relevant if validation_data
is a generator. Total number of steps (batches of samples)
to yield from validation_data generator before stopping
at the end of every epoch. It should typically
be equal to the number of samples of your
validation dataset divided by the batch size.
Optional for Sequence: if unspecified, will use
the len(validation_data) as a number of steps.

class_weight: Optional dictionary mapping class indices (integers)
to a weight (float) value, used for weighting the loss function
(during training only). This can be useful to tell the model to
"pay more attention" to samples
from an under-represented class.

max_queue_size: Integer. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.

workers: Integer. Maximum number of processes to spin up
when using process-based threading.
If unspecified, workers will default to 1. If 0, will
execute the generator on the main thread.

use_multiprocessing: Boolean.
If True, use process-based threading.
If unspecified, use_multiprocessing will default to False.
Note that because this implementation
relies on multiprocessing,
you should not pass non-picklable arguments to the generator
as they can't be passed easily to children processes.

shuffle: Boolean. Whether to shuffle the order of the batches at
the beginning of each epoch. Only used with instances
of Sequence (keras.utils.Sequence).
Has no effect when steps_per_epoch is not None.

initial_epoch: Integer.
Epoch at which to start training
(useful for resuming a previous training run).

Returns

A History object. Its History.history attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).

Raises

ValueError: In case the generator yields data in an invalid format.

Example

def generate_arrays_from_file(path):
while True:
with open(path) as f:
for line in f:
# create numpy arrays of input data
# and labels, from each line in the file
x1, x2, y = process_line(line)
yield ({'input_1': x1, 'input_2': x2}, {'output': y})
model.fit_generator(generate_arrays_from_file('/my_file.txt'),
steps_per_epoch=10000, epochs=10)

evaluate_generator

The generator should return the same kind of data
as accepted by test_on_batch.

Arguments

generator: Generator yielding tuples (inputs, targets)
or (inputs, targets, sample_weights)
or an instance of Sequence (keras.utils.Sequence)
object in order to avoid duplicate data
when using multiprocessing.

steps: Total number of steps (batches of samples)
to yield from generator before stopping.
Optional for Sequence: if unspecified, will use
the len(generator) as a number of steps.

max_queue_size: maximum size for the generator queue

workers: Integer. Maximum number of processes to spin up
when using process based threading.
If unspecified, workers will default to 1. If 0, will
execute the generator on the main thread.

use_multiprocessing: if True, use process based threading.
Note that because
this implementation relies on multiprocessing,
you should not pass
non picklable arguments to the generator
as they can't be passed
easily to children processes.

verbose: verbosity mode, 0 or 1.

Returns

Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.

predict_generator

The generator should return the same kind of data as accepted by
predict_on_batch.

Arguments

generator: Generator yielding batches of input samples
or an instance of Sequence (keras.utils.Sequence)
object in order to avoid duplicate data
when using multiprocessing.

steps: Total number of steps (batches of samples)
to yield from generator before stopping.
Optional for Sequence: if unspecified, will use
the len(generator) as a number of steps.

max_queue_size: Maximum size for the generator queue.

workers: Integer. Maximum number of processes to spin up
when using process based threading.
If unspecified, workers will default to 1. If 0, will
execute the generator on the main thread.

use_multiprocessing: If True, use process based threading.
Note that because
this implementation relies on multiprocessing,
you should not pass
non picklable arguments to the generator
as they can't be passed
easily to children processes.